1. Field of the Invention
The present invention is directed generally to vision-based guidance systems and methods and, more particularly, to vision-based systems and methods for detecting, recognizing, and localizing objects.
2. Description of the Background
In the material handling industry, dunnage, such as racks and pallets, is typically lifted, transported, and stacked by human-operated fork lift vehicles. As with most industries, however, there is an ever-increasing motivation to automate such tasks to realize the benefits associated therewith. There are several limiting factors which prevent many material handling applications from becoming substantially automated. For example, most material handling operations are performed in environments which are not conducive to prior vision-based recognition systems. Such environments include assembly factories, warehouses, truck trailers, and loading docks. These environments present problems for typical prior vision-based recognition systems because of, for example, poor lighting and obstructed images. Thus, prior vision-based recognition systems typically cannot robustly and reliably detect, recognize, and localize the objects to be manipulated by the automated system.
In order to augment the ability to detect, recognize, and localize the objects, some prior guidance systems utilize infrastructure, such as laser and inertial guidance systems, to guide the automated vehicles. Such infrastructure, however, is expensive. Furthermore, once the infrastructure is in place, the facility usually cannot be easily altered without the additional expense of modifying the infrastructure.
Accordingly, there exists a need for a guidance system for material handling vehicles or other automated vehicles which operates with minimal infrastructure. There further exists a need for a guidance system for vehicles which is capable of robustly and reliably detecting and recognizing the objects within the working environment of the vehicle, and accurately localizing objects to be manipulated.